Overview

The dataset provides detailed information about the stable isotope composition of different precipitation types (rainf, fog, throughfall). It was manually collected on up to 9 study plots on a generally weekly basis between November 2012 and November 2014. The following map shows the distribution of the study plots on the southern slopes of Mt. Kilimanjaro.

The study plots span across an altitude gradient rising from 950 m to nearly 4,000 m a.s.l. The plot IDs are the ones used within the respective research group.

PlotID Land cover
fer0 Forest Erica
fpd0 Forest Podocarpus disturbed
fpo0 Forest Podocarpus
foc0 Forest Ocotea
foc6 Forest Ocotea
flm1 Forest lower mountain
nkw1 Open area near field station
hom4 Homegarden
sav5 Savanna 
mapviewOptions(basemaps = "Esri.WorldImagery")
mapview(idata, zcol = "PlotID", legend = TRUE)

Moisture sources (- 96 hours) of the isotope samples were estimated using backward trajectory computations with the HYSPLIT model (https://www.ready.noaa.gov/HYSPLIT.php) and the R opentraj package (Thalles Santos Silva (2014). opentraj: Tools for Creating and Analysing Air Trajectory Data. R package version 1.0. https://CRAN.R-project.org/package=opentraj). Reanalysis data was taken from NCEP/NCAR version 2 ( https://www.ncdc.noaa.gov/data-access/model-data/model-datasets/reanalysis-1-reanalysis-2).

For further details see reference at the end of this document.

Datasets

Precipitation was sampled from rain gauges, fog mesh grids and throughfall installations on the study plots. Throughfall has been measured by many gauges placed across the study plot. These gauges are named “Bnn” with nn being an integer number in the field and lab records. To get the mean throughfall of the respective time slot, the gauge data must be averaged.

Sampling was carried out manually by local field staff and recorded on paper sheets. Sampling took place about every week. Two intensive sampling campaigns with multiple daily recordings took place in December 2013 and April 2014.

Folder Data
data/field_records Scans of the original paper sheets group by study plots
data/lab_records Digitized paper sheets and isotope analysis data
data/compiled_data Comprehensive dataset as Shapefile and CSV
/data/hysplit Datasets related to backward trajectory computation
/data/hysplit/data Site information used to compute backward trajectories
/data/hysplit/out/evently Hysplit backward trajectories by event
/data/hysplit/out/weekly HYSPLIT backward trajectories by week
/data/hysplit/reanalysis Reanalysis data used as input for the HYSPLIT model

The data in the compiled_data folder does not include the intensive campaigns records that are also available in the lab_records folder.

The structure of the compiled isotope dataset is as follows:

## Simple feature collection with 6 features and 12 fields
## geometry type:  POINT
## dimension:      XY
## bbox:           xmin: 310260.9 ymin: 9659338 xmax: 310260.9 ymax: 9659338
## projected CRS:  WGS 84 / UTM zone 37S
##   PlotID       Date     Time      Season PrcpTyp Elvton PrcpAmt   dO18 sddO18
## 1   fer0 2012-11-22 15:40:00 short rains     fog   3880   0.000     NA     NA
## 2   fer0 2012-11-29 14:15:00 short rains     fog   3880   0.000     NA     NA
## 3   fer0 2012-12-06 14:10:00 short rains     fog   3880   1.150 -7.310  0.050
## 4   fer0 2012-12-12 14:15:00 short rains     fog   3880   0.000     NA     NA
## 5   fer0 2012-12-20 15:10:00 short rains     fog   3880   0.000     NA     NA
## 6   fer0 2012-12-28 16:17:00 short rains     fog   3880   3.598 -7.071  0.094
##        dD   sdD dExcess                 geometry
## 1      NA    NA      NA POINT (310260.9 9659338)
## 2      NA    NA      NA POINT (310260.9 9659338)
## 3 -36.600 0.350   21.88 POINT (310260.9 9659338)
## 4      NA    NA      NA POINT (310260.9 9659338)
## 5      NA    NA      NA POINT (310260.9 9659338)
## 6 -35.258 0.222   21.31 POINT (310260.9 9659338)

The variables have the following meaning:

Column Content
PlotID Study plot ID as used within the research group
Date Date of the observation
Time Time of the observation
Season Type of rainy season
PrcpType Type of precipitation (rain, fog, tf = throughfall)
Elvton Elevation of the study plot in m a.s.l
PrcpAmt Amount of recorded precipitation (rain, fog or throughfall)
dO18 delta 18O/16O
sddO18 standard deviation of delta 18O/16O
dD delta D/H
sddD standard deviation of delta D/H
dExcess Deuterium excess

The backward trajectory information computed with the Hysplit model:

##   receptor year month day hour hour.inc    lat    lon height pressure
## 1        1 2012    11  21    0        0 -3.340 37.680 3000.0    628.5
## 2        1 2012    11  20   23       -1 -3.357 37.639 2938.5    632.5
## 3        1 2012    11  20   22       -2 -3.382 37.593 2887.7    635.3
## 4        1 2012    11  20   21       -3 -3.415 37.543 2847.4    637.3
## 5        1 2012    11  20   20       -4 -3.456 37.489 2818.3    638.7
## 6        1 2012    11  20   19       -5 -3.505 37.434 2802.1    640.1
##                 date2       date
## 1 2012-11-21 00:00:00 2012-11-21
## 2 2012-11-20 23:00:00 2012-11-21
## 3 2012-11-20 22:00:00 2012-11-21
## 4 2012-11-20 21:00:00 2012-11-21
## 5 2012-11-20 20:00:00 2012-11-21
## 6 2012-11-20 19:00:00 2012-11-21

The variables have the following meaning (see Thalles Santos Silva (2014). opentraj: Tools for Creating and Analysing Air Trajectory Data. R package version 1.0. https://CRAN.R-project.org/package=opentraj):

Column Content
receptor numeric vector
year year of the calculation
month month of the calculation
day day of the calculation
hour hour of the calculation
minute minute of the calculation
hour.inc time step (age) in hours of the computed trajectory
lat latitude position of the trajectory
lon longitude position of the trajectory
height meters above ground of the trajectory
pressure pressure level of the trajectory
date2 Date and time of the trajectory
date Start date and time of the calculation

Examples

The following figures shows the dO18 and dD values of the compiled dataset. The light grey line illustrates the global meteoric water line and the black line the respective local meteoric water line for all (top left), fog (top right), rain (bottom left) and throughfall (bottom right) samples.

facet_idata <- st_drop_geometry(idata)
facet_idata$facet <- facet_idata$PrcpTyp
facet_idata <- rbind(facet_idata, data.frame(st_drop_geometry(idata), facet = "all"))

ggplot(facet_idata, aes(x = dO18, y = dD)) +
  geom_point(aes(color = PlotID, shape = PrcpTyp)) +
  geom_abline(intercept = 10, slope = 8, color = "darkgrey") +
  geom_smooth(method = "lm", se = FALSE, color = "black") +
  facet_wrap(vars(facet)) +
  scale_color_manual(values = plotcolors) +
  theme_bw()

The following figures shows the seasonal dynamics of the dO18 and dExcess rainfall sample values of the compiled dataset.

ylim_1 <- c(0, 450)
ylim_2 <- c(-10, 35)

b <- diff(ylim_1) / diff(ylim_2)
a <- b * (ylim_1[1] - ylim_2[1])

ggplot(idata[idata$PrcpTyp == "rain", ], aes(x = Date, y = PrcpAmt)) +
  geom_bar(stat = "identity", color = "black") +
  geom_line(aes(y = a + b * dO18), color = "blue") +
  geom_point(aes(y = a + b * dO18), color = "blue") +
  geom_line(aes(y = a + b * dExcess), color = "red") +
  geom_point(aes(y = a + b * dExcess), color = "red") +
  scale_y_continuous("Rainfall (mm)", sec.axis = sec_axis(~ (. - a) / b, name = "delta 18O, d excess")) +
  facet_wrap(vars(PlotID), ncol = 2) +
  theme_bw()

The following map shows the weekly averaged hysplit trajectories.

hdata_sp <- lapply(hdata, function(i){
  return(Df2SpLines(i, crs = "+init=epsg:4326"))
})
hdata_sp <- do.call(rbind, hdata_sp)
mapview(hdata_sp, label = names(hdata))

Funding

The research was fundet by the German Research Foundation (DFG) as part of the Research Unit 1246 - Kilimanjaro ecosystems under global change (Ap 243/1‐2, Na 783/5‐2).